metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
distilhubert-finetuned-gtzan
This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 0.6665
- Accuracy: 0.82
Model description
This is a distilhubert model finetuned on gtzan for music classification.
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.4758 | 1.0 | 113 | 1.4456 | 0.56 |
| 0.9722 | 2.0 | 226 | 1.0866 | 0.65 |
| 0.8148 | 3.0 | 339 | 0.8447 | 0.79 |
| 0.531 | 4.0 | 452 | 0.7676 | 0.76 |
| 0.3591 | 5.0 | 565 | 0.6793 | 0.8 |
| 0.2623 | 6.0 | 678 | 0.6151 | 0.83 |
| 0.1858 | 7.0 | 791 | 0.6248 | 0.84 |
| 0.06 | 8.0 | 904 | 0.7053 | 0.81 |
| 0.0818 | 9.0 | 1017 | 0.6606 | 0.81 |
| 0.0498 | 10.0 | 1130 | 0.6665 | 0.82 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1